countMatrix <- ReadDataFrameFromTsv(file.name.path="../data/refSEQ_countMatrix.txt")
## ../data/refSEQ_countMatrix.txt read from disk!
# head(countMatrix)
designMatrix <- ReadDataFrameFromTsv(file.name.path="../design/all_samples_short_names_noRS2HC7.tsv")
## ../design/all_samples_short_names_noRS2HC7.tsv read from disk!
# head(designMatrix)
filteredCountsProp <- filterLowCounts(counts.dataframe=countMatrix,
is.normalized=FALSE,
design.dataframe=designMatrix,
cond.col.name="gcondition",
method.type="Proportion")
## features dimensions before normalization: 27179
## Filtering out low count features...
## 14454 features are to be kept for differential expression analysis with filtering method 3
PlotPCAPlotlyFunction(counts.data.frame=log1p(filteredCountsProp), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC1", yPCA="PC2", plotly.flag=TRUE, show.plot.flag=TRUE, prefix.plot="Prop-Un-Norm")
## [1] TRUE
Loading Negative Control Genes to normalize data
library(readxl)
sd.ctrls <- read_excel(path="../data/controls/Additional File 4 full list of BMC genomics SD&RS2.xlsx", sheet=1)
sd.ctrls <- sd.ctrls[order(sd.ctrls$adj.P.Val),]
sd.neg.ctrls <- sd.ctrls[sd.ctrls$adj.P.Val > 0.9, ]
sd.neg.ctrls <- sd.neg.ctrls$`MGI Symbol`
sd.neg.ctrls <- sd.neg.ctrls[-which(is.na(sd.neg.ctrls))]
int.neg.ctrls <- sd.neg.ctrls
neg.map <- convertGenesViaBiomart(specie="mm10", filter="mgi_symbol",
filter.values=int.neg.ctrls, c("external_gene_name",
"mgi_symbol", "entrezgene"))
neg.map.nna <- neg.map[-which(is.na(neg.map$entrezgene)),]
neg.ctrls.entrez <- as.character(neg.map.nna$entrezgene)
ind.ctrls <- which(rownames(filteredCountsProp) %in% neg.ctrls.entrez)
counts.neg.ctrls <- filteredCountsProp[ind.ctrls,]
# PlotPCAPlotlyFunction(counts.data.frame=log1p(counts.neg.ctrls), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC1", yPCA="PC2", plotly.flag=TRUE, show.plot.flag=TRUE, prefix.plot="Neg Ctrls not Norm")
Positive Control Genes
## sleep deprivation
sd.lit.pos.ctrls <- read_excel("../data/controls/SD_RS_PosControls_final.xlsx",
sheet=1)
colnames(sd.lit.pos.ctrls) <- sd.lit.pos.ctrls[1,]
sd.lit.pos.ctrls <- sd.lit.pos.ctrls[-1,]
sd.est.pos.ctrls <- read_excel("../data/controls/SD_RS_PosControls_final.xlsx",
sheet=3)
sd.pos.ctrls <- cbind(sd.est.pos.ctrls$`MGI Symbol`, "est")
sd.pos.ctrls <- rbind(sd.pos.ctrls, cbind(sd.lit.pos.ctrls$Gene, "lit"))
sd.pos.ctrls <- sd.pos.ctrls[-which(duplicated(sd.pos.ctrls[,1])),]
sd.pos.ctrls <- sd.pos.ctrls[-which(is.na(sd.pos.ctrls[,1])),]
normPropCountsUqua <- NormalizeData(data.to.normalize=filteredCountsProp,
norm.type="tmm",
design.matrix=designMatrix)
PlotPCAPlotlyFunction(counts.data.frame=log1p(normPropCountsUqua), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC1", yPCA="PC2", plotly.flag=TRUE, show.plot.flag=TRUE, prefix.plot="TMM-Norm")
## [1] TRUE
pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(as.matrix(normPropCountsUqua), outline=FALSE, col=pal[designMatrix$gcondition])
K=5
library(RUVSeq)
neg.ctrl.list <- rownames(counts.neg.ctrls)
groups <- makeGroups(designMatrix$gcondition)
ruvedSExprData <- RUVs(as.matrix(round(normPropCountsUqua)), cIdx=neg.ctrl.list,
scIdx=groups, k=5)
normExprData <- ruvedSExprData$normalizedCounts
PlotPCAPlotlyFunction(counts.data.frame=log1p(normExprData), design.matrix=designMatrix, shapeColname="genotype", colorColname="condition", xPCA="PC1", yPCA="PC2", plotly.flag=TRUE, show.plot.flag=TRUE, prefix.plot="TMM+RUV-Norm")
## [1] TRUE
pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(normExprData, outline=FALSE, col=pal[designMatrix$gcondition])
Modeling interaction term to understand differences between Knock out and Wild Type in Sleed Deprivation condition.
interactionMatrix <- constructInteractionMatrix(design.matrix=designMatrix,
genotype.col="genotype",
genotype.ref="WT",
condition.col="condition",
weights=ruvedSExprData$W)
cond <- designMatrix[["condition"]]
fit <- applyEdgeRGLMFit(counts=filteredCountsProp, factors=cond,
design=interactionMatrix, is.normalized=FALSE, method="TMM",
verbose=FALSE)
lrt <- applyEdgeRLRT(fit=fit, interaction.matrix=interactionMatrix,
interaction.term="genoKO:condSD5", verbose=FALSE)
res.o.map <- convertGenesViaBiomart(specie="mm10", filter="entrezgene",
filter.values=rownames(lrt),
c("external_gene_name", "mgi_symbol", "entrezgene"))
res.o <- attachGeneColumnToDf(mainDf=lrt,
genesMap=res.o.map,
rowNamesIdentifier="entrezgene",
mapFromIdentifier="entrezgene",
mapToIdentifier="external_gene_name")
res.o <- res.o[order(res.o$FDR),]
WriteDataFrameAsTsv(data.frame.to.save=res.o,
file.name.path=paste0("SD5_interaction_matrix", "_edgeR"))
vp <- luciaVolcanoPlot(res.o, positive.controls.df=sd.pos.ctrls,
prefix="SD5 Interaction", threshold=0.05)
ggplotly(vp)
minor.ones <- res.o[(res.o$FDR < 0.15),]
minor.ones <- minor.ones[order(minor.ones$FDR),]
geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name=minor.ones$gene[1],
res.o=res.o, show.plot=TRUE, plotly.flag=TRUE)
geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name=minor.ones$gene[2],
res.o=res.o, show.plot=TRUE, plotly.flag=TRUE)
geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name=minor.ones$gene[3],
res.o=res.o, show.plot=TRUE, plotly.flag=TRUE)
geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name=minor.ones$gene[4],
res.o=res.o, show.plot=TRUE, plotly.flag=TRUE)
geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name=minor.ones$gene[5],
res.o=res.o, show.plot=TRUE, plotly.flag=TRUE)
geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name=minor.ones$gene[6],
res.o=res.o, show.plot=TRUE, plotly.flag=TRUE)
geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name=minor.ones$gene[7],
res.o=res.o, show.plot=TRUE, plotly.flag=TRUE)
geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name=minor.ones$gene[8],
res.o=res.o, show.plot=TRUE, plotly.flag=TRUE)
geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name=minor.ones$gene[9],
res.o=res.o, show.plot=TRUE, plotly.flag=TRUE)
geneProfileLucia(normalized.counts=normExprData,
design.matrix=designMatrix,
gene.name=minor.ones$gene[10],
res.o=res.o, show.plot=TRUE, plotly.flag=TRUE)